'Distance measures' presentation slideshows

Extractors against classical and quantum adversaries

Extractors against classical and quantum adversaries. AmnonTa-Shma Tel-Aviv University. Is randomness a feasible resource?. Probabilistic algorithms assume access to truly uniform bits. Cryptography assumes one can generate truly random bits

1.02k views • 89 slides

AMCS/CS 340: Data Mining

Classification: Evaluation. AMCS/CS 340: Data Mining. Xiangliang Zhang King Abdullah University of Science and Technology. Model Evaluation. Metrics for Performance Evaluation How to evaluate the performance of a model? Methods for Performance Evaluation

1k views • 85 slides

Molecular Phylogenetics

Molecular Phylogenetics. Dan Graur. Molecular phylogenetic approaches: 1. distance-matrix (based on distance measures) 2. character-state (based on character states) 3. maximum likelihood (based on both character states and distances). DISTANCE-MATRIX METHODS

867 views • 65 slides

Text Similarity

763 views • 64 slides

Building phylognetic trees

Building phylognetic trees. Read Chapter 5. Building a tree. Aim in building a phylogenetic tree is to use a knowledge of the characters of organisms to build a tree that reflects the relationships between them.

732 views • 56 slides

CS544: Named Entity Recognition and Classification

CS544: Named Entity Recognition and Classification. January 20, 2011. Zornitsa Kozareva USC/ISI Marina del Rey, CA kozareva@isi.edu www.isi.edu/~kozareva. Named Entity Recognition and Classification. Prof. Jerry Hobbs taught CS544 during February 2010.

725 views • 52 slides

Chapter 8: Classification and Clustering Methods

Chapter 8: Classification and Clustering Methods. 8.1 Introduction 8.2 Parametric classification approaches 8.2.1 Distance between measurements 8.2.2 Statistical classification 8.2.3 OLS regression method 8.2.4 Discriminant function analysis 8.2.5 Bayesian classification

804 views • 52 slides

DATA FUSION

DATA FUSION. Resolving Inconsistencies at Schema, Tuple and Value Level Naveen Rajamoorthy Nachiappan Chidambaram Arun karthikeyan Palaniswamy Sriramakrishnan Soundarrajan. Need for Data Fusion. To compare different Data Sets Example: Shopping Agents Disaster Management System.

867 views • 48 slides

Expression analysis 2

Expression analysis 2. Introduction to Bioinformatics morten@binf.ku.dk. Program. Jeppe Vinther Array quality Finding significantly expressed genes Spreadsheet exercise dChip exercise Overrepresented gene sets dChip exercise Web exercise (DAVID) Clustering Distance measure exercise

460 views • 38 slides

Searching in text databases with non-standard orthography

Searching in text databases with non-standard orthography. Thomas Pilz University of Duisburg-Essen Digital Historical Corpora, December 3-8, 2006. Overview. Introduction Processing historical documents Text recognition Performing search operations Distance measures

375 views • 26 slides

Region-based Querying of Solar Data Using Descriptor Signatures

Region-based Querying of Solar Data Using Descriptor Signatures. Authors: Juan M. Banda 1 , Chang Liu 1 and Rafal A. Angryk 2 1 Montana State University, Bozeman, MT 2 Georgia State University, Atlanta, GA. Introduction.

258 views • 20 slides

Active Learning

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318 views • 17 slides

Lab 7. Estimating Population Structure

Lab 7. Estimating Population Structure. Goals. Estimate and interpret statistics (AMOVA + Bayesian) that characterize population structure. Demonstrate roles of gene flow and genetic drift on population structure. q m. m. m. m. m. m. q 0. q 0. q 0. q 0. q 0.

511 views • 17 slides

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications. Part 6: Nearest and k-nearest Neighbor Classification. Nearest Neighbor (NN) Rule & k-Nearest Neighbor (k-NN) Rule. Non-parametric classification rules : Linear and generalized discriminant functions

321 views • 16 slides

From: McCune, B. & J. B. Grace. 2002. Analysis of Ecological Communities . MjM Software Design, Gleneden Beach, Or

CHAPTER 11 Hierarchical Clustering. Tables, Figures, and Equations. From: McCune, B. & J. B. Grace. 2002. Analysis of Ecological Communities . MjM Software Design, Gleneden Beach, Oregon http://www.pcord.com. How it works

268 views • 15 slides

Distance Measures Tutorial Podcast

Distance Measures Tutorial Podcast. CSC 520 Digital Image Processing Dr. Karl Ricanek Jr. Distance Measures Tutorial. Overview City-block measure Chessboard measure Euclidean measure Example Euclidean, D 4 (N 4 ), and D 8 (N D ). Distance Measures Tutorial. Euclidean (L 2 )

428 views • 14 slides

Distance vs Displacement

Distance vs Displacement. Learning Target. I can explain the difference between distance and displacement. Why? In this unit we will be investigating distance compared to displacement. You should be able to say how they are different and how they are the same. 0. 1. 2. 3. 4. 5. 6.

498 views • 12 slides

What Is Cluster Analysis?

What Is Cluster Analysis?. Imagine that you are given a set of data objects for analysis where, unlike in classification, the class label of each object is not known.

689 views • 10 slides

IE 302 Recitation 3

IE 302 Recitation 3. Distance Measures. Rectilinear distance (L 1 norm) d(X, P i ) = |x - a i | + |y - b i | Straight line or Euclidean distance (L 2 norm) d(X, P i ) = Tchebyshev distance (L ? norm) d(X, P i ) = max{|x - a i |, |y - b i |}. P i = (a i , b i ). X = (x, y).

212 views • 8 slides